Source-network-load multi-dimensional typical mountain power distribution network photovoltaic bearing capacity evaluation method and system

Abstract

The invention discloses a source network load multi-dimensional typical mountain power distribution network photovoltaic bearing capacity evaluation method and system, and relates to the technical field of new energy, and the method comprises the steps: recognizing a source network load multi-dimensional typical mountain, and collecting the power grid operation data of a multi-dimensional typical mountain power distribution network for preprocessing; and constructing a photovoltaic bearing capacity evaluation model, evaluating the photovoltaic bearing capacity of the source network load multi-dimensional typical mountain power distribution network, and analyzing and adjusting a photovoltaic bearing capacity evaluation result. The method has high adaptability and expandability, can be customized and optimized according to actual requirements of different regions, has wide actual application value, and particularly has a remarkable promotion effect on optimizing power grid operation and improving the stability and economical efficiency of a photovoltaic system under the background of large-scale access of photovoltaic power generation.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and specifically to a method and system for evaluating the photovoltaic carrying capacity of a typical mountain distribution network in multiple dimensions of source, grid and load. Background Art

[0002] With the continuous growth of global energy demand and the improvement of environmental protection awareness, photovoltaic power generation, as a clean and renewable energy form, has received widespread attention and application. In this context, photovoltaic power generation technology has developed rapidly and has gradually been applied in distribution networks. With the increase in the proportion of photovoltaic power generation in the power system, the indirect challenges to the operation of distribution networks have become increasingly apparent, especially in mountainous areas with complex geographical conditions. Due to the diversity of terrain, variability of climate and complexity of power grid structure, the carrying capacity assessment of photovoltaic power generation has become a very challenging task.

[0003] The existing technologies have the following shortcomings in the evaluation of photovoltaic carrying capacity in multiple dimensions of source, grid and load. Most of the existing evaluation methods rely on the analysis of a single or a few parameters, ignoring the mutual coupling relationship between parameters of different dimensions, and cannot accurately reflect the complex operating conditions in mountain distribution networks. In the process of data processing, traditional methods often fail to fully consider the dynamic changes of environmental factors, resulting in a large deviation between the evaluation results and the actual situation. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that most of the existing evaluation methods rely on the analysis of a single or a few parameters, ignoring the mutual coupling relationship between parameters of different dimensions, and cannot accurately reflect the complex operating status of mountain distribution networks. In the process of data processing, traditional methods often fail to fully consider the dynamic changes of environmental factors, resulting in a large deviation between the evaluation results and the actual situation.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a source-grid-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity evaluation method, comprising:

[0007] Identify multi-dimensional typical mountainous areas of source, grid and load, and collect multi-dimensional typical mountainous distribution network operation data for pre-processing;

[0008] Construct a photovoltaic carrying capacity assessment model to assess the photovoltaic carrying capacity of a typical mountain distribution network in multiple dimensions of source, grid and load, and analyze and adjust the photovoltaic carrying capacity assessment results;

[0009] Build a visual interface to display the results of PV carrying capacity in real time, store the data generated during the data asset assessment process and perform access control.

[0010] As a preferred solution of the source-grid-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity assessment method described in the present invention, wherein: the grid operation data includes grid operation stability, grid load and photovoltaic access data;

[0011] The grid operation data is compared with the typical mountain range one by one. If the grid operation data meets the typical mountain range at the same time, it is determined to be a typical mountain with multiple dimensions of source, grid and load; if the grid operation data does not meet the typical mountain range, it is determined to be a common mountain with multiple dimensions of source, grid and load;

[0012] The identifying of multi-dimensional typical mountains of source, grid and load includes setting a typical mountain range based on historical grid operation data and expert experience, and the typical mountain range includes grid operation stability, grid load and photovoltaic access range.

[0013] As a preferred solution of the photovoltaic carrying capacity assessment method of the source-grid-load multi-dimensional typical mountain distribution network described in the present invention, wherein: the collection of grid operation data of the multi-dimensional typical mountain distribution network includes installing smart meters in photovoltaic power stations within the source-grid-load multi-dimensional typical mountain distribution network area, collecting photovoltaic power generation power, power load, power consumption data and voltage data of each power equipment in real time, and installing solar radiation meters and temperature sensors in photovoltaic power stations to collect environmental data in real time, including solar radiation intensity and temperature data;

[0014] The spatial location of each power device is collected through GPS equipment.

[0015] As a preferred solution of the source-grid-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity evaluation method described in the present invention, wherein: the preprocessing includes calculating the load demand data based on the collected power consumption data ;

[0016] Linear interpolation was used to fill in missing data for photovoltaic power generation, power load, power consumption, voltage, environment, and load demand data;

[0017] Use the z-score method to detect and delete outliers in the photovoltaic power generation, power load, power consumption, voltage, environment and load demand data;

[0018] Use a moving average filter to denoise the photovoltaic power generation, power load, power consumption, voltage, environment and load demand data;

[0019] The denoised photovoltaic power generation, power load, power consumption, voltage, environment and load demand data are standardized.

[0020] As a preferred solution of the photovoltaic carrying capacity evaluation method of a typical mountain distribution network with multiple dimensions of source, grid and load described in the present invention, wherein: the photovoltaic carrying capacity evaluation model is constructed to evaluate the photovoltaic carrying capacity of a typical mountain distribution network with multiple dimensions of source, grid and load, including using a constant temperature environment laboratory to design and construct an optimal temperature laboratory, using a precise temperature control adjustment method to set the initial temperature, gradually increasing the temperature while keeping the solar radiation power unchanged, recording the output power of the photovoltaic temperature, and calculating the efficiency of the photovoltaic at each temperature point. ;

[0021] The calculated efficiency value is matched with the temperature, and the efficiency-temperature curve is drawn. The temperature corresponding to the highest efficiency point is selected as the optimal operating temperature of the photovoltaic system;

[0022] Calculate environmental impact factors based on standardized environmental data , the formula is:

[0023] ;

[0024] in, is the solar radiation intensity of the ith power equipment at time t, is the standard solar radiation intensity, is the temperature of the ith power device at time t, is the optimal operating temperature of the photovoltaic system, is the temperature range of the photovoltaic system;

[0025] Calculate the ratio A of the photovoltaic power generation power of the power equipment to the load demand, the formula is:

[0026] ;

[0027] in, is the photovoltaic power generation power of the ith power device at time t, N is the total number of power devices, is the load demand of the ith power equipment at time t, is a very small positive number;

[0028] The standardized voltage data is constructed into a probability distribution, and the voltage value with the highest frequency is selected as the reference voltage;

[0029] Calculate the voltage change by subtracting the reference voltage from the actual voltage of the power equipment ;

[0030] The voltage change is linearly transformed B, and the formula is:

[0031] ,

[0032] in, It is the reference voltage value of the power grid under normal working condition;

[0033] Calculate the second derivative of load data ,in is the load data at position x and time t;

[0034] Calculate the spatial derivative of photovoltaic power data ,in is the photovoltaic power data at position x and time t;

[0035] The second-order derivative of load data and the spatial derivative of photovoltaic power data are combined to construct the comprehensive impact factor C of spatial variation. The formula is:

[0036] ,

[0037] in, and are the minimum and maximum spatial positions of the distribution network, respectively, and x is the spatial position;

[0038] Construct a photovoltaic carrying capacity evaluation model to evaluate the photovoltaic carrying capacity of the i-th power equipment in a typical mountain distribution network with multiple dimensions of source, grid and load. , the formula is:

[0039] ,

[0040] in, and are the start and end time of the evaluation period, respectively.

[0041] As a preferred solution of the source-grid-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity assessment method of the present invention, wherein: the analysis and adjustment of the photovoltaic carrying capacity assessment result includes randomly collecting historical data within a period of time from the actually operating distribution network;

[0042] Use the weighted average method to calculate the actual observed values ​​of historical data;

[0043] The historical data includes photovoltaic power generation data, load demand data, environmental data and voltage data and is pre-processed to generate a test set;

[0044] Inputting the test set into the photovoltaic carrying capacity evaluation model to obtain an evaluation result of the photovoltaic carrying capacity;

[0045] The observation threshold is set based on the historical observation value, and the error value is obtained by subtracting the actual observation value from the evaluation result of the photovoltaic carrying capacity. The error value is compared with the observation threshold. If the error value is greater than the observation threshold, the temperature range in the environmental impact factor is adjusted; if the error value is less than or equal to the observation threshold, continuous observation is carried out.

[0046] As a preferred solution of the photovoltaic carrying capacity assessment method of a typical mountain distribution network with multiple dimensions of source, grid and load described in the present invention, wherein: the construction of a visualization interface to display the results of photovoltaic carrying capacity in real time includes using D3.js to build a visualization interface, using a data visualization tool to display the photovoltaic carrying capacity assessment results and error values ​​in real time, and using Plotly.js to draw a line graph of the photovoltaic carrying capacity assessment results that is updated in real time for real-time display;

[0047] The data generated during the storage of data asset assessment and access control include storing photovoltaic power generation power, power load, power consumption, voltage, environment, load demand data and photovoltaic carrying capacity assessment results in the database in chronological order, and setting security access measures. The database will back up the stored data in the cloud and regularly perform integrity checks on the stored data and backup data.

[0048] A photovoltaic carrying capacity assessment system for a typical mountain distribution network using a multi-dimensional source-grid-load method, wherein:

[0049] The acquisition unit identifies multi-dimensional typical mountainous areas of source, grid and load, and collects the grid operation data of multi-dimensional typical mountainous distribution networks for pre-processing;

[0050] The analysis unit builds a photovoltaic carrying capacity assessment model to assess the photovoltaic carrying capacity of a typical mountain distribution network in multiple dimensions of source, grid and load, and analyzes and adjusts the photovoltaic carrying capacity assessment results;

[0051] The visualization unit builds a visualization interface to display the results of PV carrying capacity in real time, stores the data generated during the data asset assessment process and performs access control.

[0052] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein: the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0053] A computer-readable storage medium stores a computer program, wherein: when the computer program is executed by a processor, the steps of any one of the methods of the present invention are implemented.

[0054] Beneficial effects of the present invention: The present invention extracts and standardizes multiple operations from the power grid operation data, and also combines the calculation of environmental impact factors and the analysis of the ratio of photovoltaic power to load demand, providing a scientific basis for the adaptation of photovoltaic systems under specific geographical and climatic conditions. The accuracy and reliability of the data are ensured by applying efficient data preprocessing methods and outlier detection technology. The construction of a visual interface enables the real-time display of photovoltaic carrying capacity assessment results, helping operators to grasp the system status in a timely manner and make adjustments when the error exceeds the standard. Through regular data storage and cloud backup, the long-term preservation and security of data can also be ensured. In addition, the present invention has high adaptability and scalability, can be customized and optimized according to the actual needs of different regions, and has a wide range of practical application value, especially in the context of large-scale access to photovoltaic power generation, it has a significant promoting effect on optimizing power grid operation and improving the stability and economy of photovoltaic systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0056] Figure 1 An overall flow chart of a method for evaluating photovoltaic carrying capacity of a typical mountain distribution network in multiple dimensions of source, grid and load provided in the first embodiment of the present invention;

[0057] Figure 2 A schematic diagram of the analysis process of a method for evaluating the photovoltaic carrying capacity of a typical mountain distribution network in multiple dimensions of source, grid and load provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0059] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a method for evaluating the photovoltaic carrying capacity of a typical mountain distribution network in multiple dimensions of source, grid and load. The method for evaluating the photovoltaic carrying capacity of a typical mountain distribution network in multiple dimensions of source, grid and load includes:

[0060] S1: Identify typical mountain areas with multiple dimensions of source, grid and load, and collect grid operation data of typical mountain distribution networks with multiple dimensions for preprocessing.

[0061] Specifically, identifying typical mountain areas with multiple dimensions of source, grid and load means collecting historical grid operation data, including grid operation stability, grid load and photovoltaic access data. The typical mountain area range is set based on historical grid operation data and expert experience, including grid operation stability, grid load and photovoltaic access range.

[0062] The grid operation data is compared with the typical mountain range one by one. If the grid operation data meets the typical mountain range at the same time, it is determined to be a typical mountain with multiple dimensions of source, grid and load. If the grid operation data does not meet the typical mountain range, it is determined to be a common mountain with multiple dimensions of source, grid and load. The identification of the typical mountain with multiple dimensions of source, grid and load includes setting the typical mountain range based on historical grid operation data and expert experience. The typical mountain range includes grid operation stability, grid load and photovoltaic access range.

[0063] The accumulation and analysis of historical data can reveal long-term trends and seasonal changes in grid operation, and help determine which areas have the potential to become typical multi-dimensional mountains of source, grid and load. By combining historical data analysis with expert experience to set the range of typical mountains, areas with high potential can be accurately defined in different dimensions. These ranges, as the basis for judgment, can significantly improve the accuracy of identifying multi-dimensional typical mountains of source, grid and load, and reduce the risk of misjudgment or omission. Through precise on-site data collection, it can more realistically reflect the grid operation status of the area, provide high-quality basic data for subsequent analysis and judgment, and ensure the reliability and accuracy of the identification results, making the premise of photovoltaic carrying capacity assessment more solid. The multi-dimensional grid operation data of source, grid and load in the target area can be collected, which can provide dynamic grid operation status feedback. Compared with historical data, real-time data can reflect the current operating status and its changing trends, especially in terms of volatility caused by photovoltaic access. This plays an important role in dynamic monitoring for identifying typical mountains. By comparing the real-time collected data with the preset typical mountain range one by one, it is possible to efficiently and accurately determine whether the area is a multi-dimensional typical mountain in terms of source, grid and load. This comparison method can quickly screen out representative areas and provide decision support for the optimized operation of the power grid.

[0064] Furthermore, collecting grid operation data of a multi-dimensional typical mountain distribution network refers to installing smart meters in photovoltaic power stations within the source-grid-load multi-dimensional typical mountain distribution network area to collect real-time photovoltaic power generation power, power load, power consumption and voltage data of each power equipment, and installing solar radiation meters and temperature sensors in photovoltaic power stations to collect real-time environmental data, including solar radiation intensity and temperature data.

[0065] The spatial location of each power device is collected through GPS equipment.

[0066] Real-time collection and real-time monitoring of the distribution network operation status ensures the high timeliness and high accuracy of the data. By real-time monitoring of temperature data and solar radiation intensity data, the operation strategy of the photovoltaic system can be adjusted in time to ensure that the system operates within the optimal temperature range, thereby maximizing power generation efficiency, reducing power generation losses caused by temperature fluctuations, and improving the overall economic benefits of the system. Real-time operation data of the distribution network in multiple dimensions are obtained, including photovoltaic power generation power, power load, power consumption, voltage, solar radiation intensity and temperature data. These data can not only reflect the operating status of each dimension respectively, but also reveal the relationship and influence between the dimensions through integrated analysis, thereby providing rich and detailed data support for the comprehensive evaluation of photovoltaic carrying capacity. The integrated application of this multi-dimensional data enables the photovoltaic carrying capacity evaluation model to more comprehensively consider the complex factors in actual operation and improve the reliability and applicability of the evaluation results.

[0067] Furthermore, preprocessing refers to calculating load demand data based on the collected power consumption data. , the formula is:

[0068] ;

[0069] in is the j-th power consumption data of power equipment i at time t, and M is the total number of power consumption data.

[0070] Linear interpolation was used to fill missing data in the photovoltaic power generation, power load, power consumption, voltage, environment, and load demand data. The z-score method was used to detect and delete outliers in the photovoltaic power generation, power load, power consumption, voltage, environment, and load demand data. A moving average filter was used to denoise the photovoltaic power generation, power load, power consumption, voltage, environment, and load demand data. The denoised photovoltaic power generation, power load, power consumption, voltage, environment, and load demand data were standardized.

[0071] Through accurate load demand forecasting, the operating efficiency of the power system is optimized, energy waste is reduced, and the reliability and stability of the overall system are improved. The linear interpolation method is simple and efficient, and can provide accurate supplementary data when there is less missing data, thereby maintaining the consistency of the data set. By filling in missing data, the integrity of the data is improved, making subsequent analysis and modeling more accurate, and ultimately improving the reliability of the entire evaluation process. By removing outliers, the quality of the data is significantly improved, and the analysis deviation caused by outliers is reduced. By cleaning up outliers in the data, the representativeness of the data is ensured, and the accuracy of the model evaluation is further improved. By smoothing the data, short-term random fluctuations are reduced, making the data more representative and predictable. Through denoising, smoother time series data is obtained, which improves the interpretability of the data and the predictive ability of the model, and ultimately enhances the accuracy of the photovoltaic carrying capacity assessment. Through standardization, the data is more uniform, eliminating the impact of dimensional differences on the model results, and improving the uniformity of the data and the effectiveness of the analysis.

[0072] S2: Construct a photovoltaic carrying capacity assessment model to evaluate the photovoltaic carrying capacity of a typical mountain distribution network in multiple dimensions of source, grid and load, and analyze and adjust the photovoltaic carrying capacity assessment results.

[0073] Specifically, a photovoltaic carrying capacity evaluation model is constructed to evaluate the photovoltaic carrying capacity of a typical mountain distribution network in multiple dimensions of source, grid and load. This includes using a constant temperature environment laboratory to design and build an optimal temperature laboratory, using a precise temperature control method to set the initial temperature, gradually increasing the temperature while keeping the solar radiation power constant, recording the photovoltaic temperature output power, and calculating the photovoltaic efficiency at each temperature point. , the formula is:

[0074] ;

[0075] in is the input solar radiation power, is the output power of the photovoltaic at temperature T.

[0076] The calculated efficiency value is matched with the temperature, and the efficiency-temperature curve is drawn. The temperature corresponding to the highest efficiency point is selected as the optimal operating temperature of the photovoltaic system.

[0077] Calculate environmental impact factors based on standardized environmental data , the formula is:

[0078] ;

[0079] in is the solar radiation intensity of the ith power equipment at time t, is the standard solar radiation intensity, is the temperature of the ith power device at time t, is the optimal operating temperature of the photovoltaic system, is the temperature range of the photovoltaic system.

[0080] Existing technologies usually rely on fixed environmental correction factors and lack real-time response to actual environmental conditions. The environmental impact factor construction method combines the actual measured optimal operating temperature and dynamic environmental data, and flexibly adjusts the exponential function to significantly enhance the adaptability of the model. Photovoltaic efficiency usually does not decrease linearly, but as the temperature deviation increases, the rate of efficiency decreases faster. Using an exponential decay model to describe this nonlinear relationship is the most reasonable choice. Existing technologies usually rely on fixed environmental correction factors and lack real-time response to actual environmental conditions. It is difficult to accurately reflect the impact of environmental changes at different times and places on photovoltaic systems.

[0081] Calculate the ratio A of the photovoltaic power generation power of the power equipment to the load demand, the formula is:

[0082] ;

[0083] in is the photovoltaic power generation power of the ith power device at time t, N is the total number of power devices, is the load demand of the ith power equipment at time t, is a very small positive number.

[0084] Directly comparing the absolute values ​​of power generation and load demand cannot effectively reflect the relative balance between them, especially when load demand and power generation fluctuate greatly, which may lead to distortion of evaluation. Considering the data of photovoltaic power generation or load demand alone and ignoring their relationship may lead to deviation in the evaluation of system carrying capacity. The ratio can not only reflect the ability of photovoltaic power generation to meet load demand, but also provide a direct, dimensionless evaluation index, which is suitable for comparison between different power equipment. The existing technology uses average values ​​or historical data for static evaluation, lacks sensitivity to real-time dynamic changes, and only considers the photovoltaic power generation capacity under standard conditions, and fails to fully consider the impact of environmental factors on power generation.

[0085] The standardized voltage data is constructed into a probability distribution diagram, and the voltage value with the highest frequency is selected as the reference voltage. The voltage change is calculated by subtracting the reference voltage from the actual voltage of the power equipment. , the formula is:

[0086] ;

[0087] in is the actual voltage of the ith power device at time t, It is the reference voltage value of the power grid under normal working conditions.

[0088] The voltage change is linearly transformed B, and the formula is:

[0089] ;

[0090] The use of linear transformation cannot capture the nonlinear characteristics of the impact of voltage changes on the system, especially when the voltage fluctuates greatly. The linear method may not be able to reflect the cumulative effect of voltage changes and the complex impact on system stability, resulting in large deviations in the evaluation results in practical applications. The nonlinear relationship ensures the adaptability of the model to voltage fluctuations of varying degrees, making the evaluation of photovoltaic carrying capacity more in line with the complexity of actual grid operation. The use of direct linear form will weaken the model's ability to respond to larger voltage fluctuations and cannot accurately reflect the cumulative effect of voltage changes in the system. The linear form handles positive and negative voltage deviations asymmetrically. Through square processing, the formula reacts more violently to voltage changes, especially when the fluctuation is large, and can accurately capture its impact on the system. The square operation has smooth nonlinear characteristics, which helps to control the output stability of the system. The introduction of nonlinear functions makes the model respond smoothly to small voltage changes and quickly to large voltage changes, accurately reflecting the dynamic needs of actual grid operation.

[0091] Calculate the second derivative of load data , the formula is:

[0092] ;

[0093] in The space between electrical equipment. is the load data at position x+Δx and time t, is the load data at position x and time t, is the load data at position x-Δx and time t.

[0094] Calculate the spatial derivative of photovoltaic power data , the formula is:

[0095] ;

[0096] in is the photovoltaic power data at position x+Δx and time t, is the photovoltaic power data at position x-Δx and time t.

[0097] The second-order derivative of load data and the spatial derivative of photovoltaic power data are combined to construct the comprehensive impact factor C of spatial variation. The formula is:

[0098] ;

[0099] in and are the minimum and maximum spatial positions of the distribution network respectively, and x is the spatial position.

[0100] Analyzing only the spatial distribution of load power cannot fully reflect the impact of photovoltaic power access on the entire system, especially when there is a spatial mismatch between photovoltaic power generation and load demand. Considering the load alone will miss the key impact of the photovoltaic system. Analyzing only the spatial variation of photovoltaic power is also insufficient to fully understand its impact on grid stability, especially in areas where the load distribution is uneven or there are significant changes. Ignoring the load distribution will lead to inaccurate evaluation results. Using a simple weighted average method instead of integration may not capture the cumulative effect of spatial distribution. Especially when the load and photovoltaic system are unevenly distributed, the evaluation results are easily distorted. The integration operation can accumulate and quantify the spatial variation effects within the entire distribution network, providing a global comprehensive The evaluation index reflects the impact of the distribution of load and photovoltaic power generation in the entire system on stability. Through square root function processing, the weight of the spatial variation of photovoltaic power in the overall impact is enhanced, while avoiding excessive sensitivity to small changes, making the model response smoother and more stable. Through the combination of second-order and first-order derivatives, this scheme can simultaneously analyze the spatial distribution characteristics of load and photovoltaic power, and more comprehensively reflect the complex dynamics of the distribution network. Through integration of the entire spatial range, this scheme can comprehensively evaluate the change effects of all spatial locations, avoiding the limitations of single-point or local analysis. Through square root processing, this scheme can enhance the flexible response capability of the model while avoiding excessive amplification of small changes, providing more reliable evaluation results.

[0101] Construct a photovoltaic carrying capacity assessment model to evaluate the photovoltaic carrying capacity of power equipment in a typical mountain distribution network with multiple dimensions of source, grid and load , the formula is:

[0102] ;

[0103] in and are the start and end time of the evaluation period, respectively.

[0104] Time integration is the only method that can accumulate dynamic changes to an overall effect, while simple weighted average or instantaneous value superposition will ignore the continuity and cumulative effects of changes in the time dimension. Many methods in the existing technology only analyze a specific moment or average value, and lack sensitivity to the dynamic changes of the system. Through time integration, the model can capture the dynamic changes in the system operation and provide a more accurate assessment of the photovoltaic carrying capacity, especially in the case of large load fluctuations. Existing technologies often only focus on one or several single-dimensional factors and lack comprehensive integrated analysis, resulting in one-sided evaluation results. Through comprehensive analysis of multi-dimensional factors, it can provide a global assessment of the operation status of the power grid, avoid deviations caused by ignoring key factors, improve the integrity of the assessment, and effectively evaluate the photovoltaic carrying capacity under different operating scenarios and conditions to adapt to more complex actual situations.

[0105] Identifying the optimal operating temperature of the photovoltaic system allows the photovoltaic system to operate under optimal conditions, thereby maximizing power generation efficiency and extending the service life of the system. It not only improves the economy of the photovoltaic system, but also enhances the adaptability of the system under different environmental conditions. It converts complex environmental variables into quantifiable indicators, providing a scientific basis for the optimization of the photovoltaic system, so that the operating parameters of the photovoltaic system can be dynamically adjusted to adapt to actual environmental conditions, ensuring that the system can maintain efficient operation under various conditions. The calculation of the ratio makes the resource allocation of the power system more reasonable, reduces unnecessary power waste, and improves the power supply reliability of the overall system. It accurately identifies weak power equipment in the power grid and foresees potential power supply shortages, thereby providing key data support for power dispatching and accurately capturing the spatial variation characteristics of the power grid operation, especially in complex mountainous terrain. By comprehensively considering the variation factors in the spatial and temporal dimensions, the system can achieve a more accurate photovoltaic carrying capacity assessment and ensure the stability and reliability of the distribution network in actual operation.

[0106] Furthermore, analyzing and adjusting the photovoltaic carrying capacity assessment results refers to randomly collecting historical data over a period of time from the actual distribution network, and using the weighted average method to calculate the actual observation value of the historical data; the historical data includes photovoltaic power generation data, load demand data, environmental data and voltage data and is pre-processed to generate a test set.

[0107] The test set is input into the photovoltaic carrying capacity assessment model to obtain the evaluation results of the photovoltaic carrying capacity.

[0108] The observation threshold is set based on the historical observation value, and the error value is obtained by subtracting the actual observation value from the evaluation result of the photovoltaic load capacity. The error value is compared with the observation threshold. If the error value is greater than the observation threshold, the temperature range in the environmental impact factor is adjusted. If the error value is less than or equal to the observation threshold, continuous observation is performed. The specific process is as follows: Figure 2 shown.

[0109] The generation and application of the test set can provide real operating data for the evaluation model, verify the performance of the model under different environmental and load conditions, ensure the robustness and adaptability of the model, enable the model to maintain high accuracy and stability in practical applications, avoid misjudgment caused by overfitting or data bias, and quantify the error to objectively evaluate the prediction ability of the model and timely discover the deviations and deficiencies in the model. The dynamic adjustment mechanism ensures the adaptive ability of the model, enables the model to be continuously optimized according to actual conditions, and improves its prediction accuracy. By optimizing the temperature range, the model can better adapt to actual environmental changes, thereby improving the accuracy of photovoltaic carrying capacity assessment. The adjustment not only enhances the flexibility of the model, but also provides a feasible parameter optimization path for the application of similar systems in the future, ensuring that the best evaluation results can be obtained in different environments. Through long-term monitoring, the potential performance degradation or environmental changes on the model prediction accuracy can be discovered in time, so that corresponding adjustments and optimizations can be made. This continuous observation mechanism ensures the stability and reliability of the model in actual operation, and provides important data support and experience accumulation for future system upgrades and improvements.

[0110] S3: Build a visual interface to display the results of PV carrying capacity in real time, store the data generated during the data asset assessment process and perform access control.

[0111] Specifically, building a visualization interface to display the results of photovoltaic carrying capacity in real time means using D3.js to build a visualization interface, using data visualization tools to display the photovoltaic carrying capacity evaluation results and error values ​​in real time, and using Plotly.js to draw a line graph of the photovoltaic carrying capacity evaluation results that is updated in real time for real-time display.

[0112] Among them, D3.js (Data-Driven Documents) is a data-driven JavaScript library for generating complex, dynamic and interactive data visualizations. D3.js allows developers to control the content and appearance of a page by binding data to DOM elements (such as SVG, Canvas, etc.). It supports multiple types of visualization charts (such as bar charts, line charts, scatter charts, tree charts, etc.), and provides powerful functions for data processing, conversion and formatting. Plotly.js is an open source JavaScript chart library built on D3.js and stack.gl, specifically for quickly building beautiful and interactive charts. Unlike D3.js, Plotly.js provides a more simplified API that supports the rapid generation of interactive charts and dashboards. It has a rich set of built-in chart types (such as line charts, bar charts, pie charts, scatter charts, etc.), and can be easily integrated into web pages.

[0113] Dynamically generate and update the photovoltaic carrying capacity assessment results and error values, so that users can obtain key data instantly, improve the readability and comprehensibility of the data, and enable users to quickly identify and analyze the performance of the photovoltaic system. The dynamic visualization function of D3.js can significantly improve work efficiency and reduce misjudgments caused by information asymmetry. Through the intuitive display of error values, the performance of the assessment model can be monitored in real time, so that adjustments and optimizations can be made when necessary. This not only improves the efficiency of information transmission, but also provides an intuitive basis for the continuous optimization of the assessment results, which helps to ensure the optimal performance of the photovoltaic system in complex environments. By updating the chart in real time, users can identify any abnormal changes in time and take corresponding measures quickly, which improves the flexibility of data analysis and enables users at different levels to obtain the required information according to their own needs, and then make more accurate judgments. Through this comprehensive display and interactive function, the system further improves its user-friendliness and application value.

[0114] Furthermore, storing the data generated during the data asset assessment process and performing access control means storing the photovoltaic power generation power, power load, power consumption, voltage, environment, load demand data and photovoltaic carrying capacity assessment results in the database in chronological order, and setting security access measures. The database will back up the stored data in the cloud and regularly perform integrity checks on the stored data and backup data.

[0115] Storing data in chronological order can facilitate subsequent analysis and tracking, and ensure the integrity and consistency of the data. Through this systematic storage method, the long-term operation of the photovoltaic system can be comprehensively monitored, and basic data support can be provided for historical data analysis and prediction. Through strict access control, illegal access to data and potential security threats are prevented, and sensitive data of the photovoltaic system is protected from external attacks or internal leaks. Security access measures also make data management more standardized and transparent, improving the overall security of the system. Cloud backup not only provides double protection for data, but also improves the durability and recoverability of data. Data recovery through cloud backup ensures that the evaluation data of the photovoltaic system will not be lost or damaged, thereby maintaining the normal operation of the system. Through regular testing, data damage or anomalies can be discovered and repaired in a timely manner, ensuring the continuity and accuracy of data, enhancing the security and reliability of data assets, and providing a strong guarantee for the long-term stable operation of the photovoltaic system.

[0116] On the other hand, this embodiment also provides a source-grid-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity assessment system, which includes:

[0117] The acquisition unit identifies multi-dimensional typical mountainous areas of source, grid and load, and collects the grid operation data of multi-dimensional typical mountainous distribution networks for preprocessing.

[0118] The analysis unit constructs a photovoltaic carrying capacity assessment model, evaluates the photovoltaic carrying capacity of a typical mountain distribution network in multiple dimensions of source, grid and load, and analyzes and adjusts the photovoltaic carrying capacity assessment results.

[0119] The visualization unit builds a visualization interface to display the results of PV carrying capacity in real time, stores the data generated during the data asset assessment process and performs access control.

[0120] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0121] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0122] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0123] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0124] Example 2 is an embodiment of the present invention, which provides a method for evaluating the photovoltaic carrying capacity of a typical mountain distribution network in multiple dimensions of source, grid and load. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0125] This embodiment designs three different environmental conditions (temperate zone, mountainous area, and desert) for evaluating photovoltaic load capacity, and uses two methods (the prior art method and the method of the present invention) to test the same power grid under these three environments. The goal is to verify the stability of the two methods for evaluating photovoltaic load capacity.

[0126] Environmental Design

[0127] Temperate environment: A typical temperate region with distinct four seasons, mild climate, moderate temperature, and relatively stable solar radiation intensity.

[0128] Mountain environment: High altitude areas with large temperature differences, low solar radiation intensity and large weather fluctuations.

[0129] Desert environment: hot weather, small daily temperature difference, extremely high solar radiation intensity, and dry climate.

[0130] The grid carrying capacity under each environmental condition remains the same, so there should be no substantial difference in the grid's photovoltaic carrying capacity in the three different environments. The stability of the evaluation results of the two methods in different environments is mainly examined.

[0131] Implementation process:

[0132] Environmental setup: Set up the power grid and conduct tests in temperate, mountainous, and desert environments. Ensure that the PV system load capacity in each environment is the same to ensure comparability of the evaluation results.

[0133] Data collection: Install smart meters, radiometers, temperature sensors and other equipment to collect real-time data on photovoltaic power generation, voltage, temperature, solar radiation intensity, etc.

[0134] Data processing and analysis:

[0135] Existing technical method: directly calculate the ratio of photovoltaic power generation to load demand to obtain an evaluation value.

[0136] The method of the present invention is to adjust the evaluation model according to environmental data (temperature, radiation intensity, etc.) and calculate the corresponding photovoltaic carrying capacity.

[0137] Experimental comparison: The evaluation results of the method of the present invention and the prior art method under three environments were compared to evaluate the stability of the two methods, as shown in Table 1 and Table 2.

[0138] Table 1 Test results of prior art methods

[0139] Table 2 Test results of the method of the present invention

[0140] The evaluation results of the traditional method show large fluctuations. For example, in a temperate environment, the evaluation value is 1.06, while in a mountainous environment, the evaluation value is 1.18, and the evaluation value in a desert environment further rises to 1.24. This shows that the traditional method fails to effectively consider the impact of the environment, resulting in large fluctuations in the evaluation results in different environments. The traditional method fails to dynamically adjust environmental factors, resulting in its evaluation results being highly sensitive to environmental changes and fluctuating greatly.

[0141] In contrast, the evaluation results of the method of the present invention show smaller fluctuations. In a temperate environment, the evaluation value is 1.08, in a mountainous environment it is 1.09, and in a desert environment it is 1.07. This shows that the method of the present invention can adapt to changes in different environmental conditions and dynamically adjust the evaluation model, thereby maintaining relatively consistent evaluation results in the three environments. Although there are small fluctuations, these fluctuations are within a reasonable range and can more accurately reflect the photovoltaic carrying capacity.

[0142] It can be seen from the data that the method of the present invention has significantly smaller fluctuations than the traditional method. The average evaluation value of the traditional method is 1.20, while the average evaluation value of the method of the present invention is 1.07, and the fluctuation range is significantly reduced. This shows that the method of the present invention can better adapt to environmental changes, thereby providing a more stable and reliable photovoltaic carrying capacity evaluation result.

[0143] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-dimensional source-grid-load photovoltaic carrying capacity assessment method for typical mountain distribution networks, characterized by: include: Identify multi-dimensional typical mountainous areas of source, grid and load, and collect multi-dimensional typical mountainous distribution network operation data for pre-processing; Construct a photovoltaic carrying capacity assessment model to assess the photovoltaic carrying capacity of a typical mountain distribution network in multiple dimensions of source, grid and load, and analyze and adjust the photovoltaic carrying capacity assessment results; Build a visual interface to display the results of PV carrying capacity in real time, store the data generated during the data asset assessment process and perform access control; The photovoltaic carrying capacity evaluation model is constructed to evaluate the photovoltaic carrying capacity of a typical mountain distribution network in multiple dimensions of source, grid and load, including using a constant temperature environment laboratory to design and construct an optimal temperature laboratory, using a precise temperature control method to set the initial temperature, gradually increasing the temperature while keeping the solar radiation power constant, recording the output power of the photovoltaic temperature, and calculating the efficiency of the photovoltaic at each temperature point. ; The calculated efficiency value is matched with the temperature, and the efficiency-temperature curve is drawn. The temperature corresponding to the highest efficiency point is selected as the optimal operating temperature of the photovoltaic system; Calculate environmental impact factors based on standardized environmental data , the formula is: ； in, is the solar radiation intensity of the ith power equipment at time t, is the standard solar radiation intensity, is the temperature of the ith power device at time t, is the optimal operating temperature of the photovoltaic system, is the temperature range of the photovoltaic system; Calculate the ratio A of the photovoltaic power generation power of the power equipment to the load demand, the formula is: ； in, is the photovoltaic power generation power of the ith power device at time t, N is the total number of power devices, is the load demand of the ith power equipment at time t, is a very small positive number; The standardized voltage data is constructed into a probability distribution, and the voltage value with the highest frequency is selected as the reference voltage; Calculate the voltage change by subtracting the reference voltage from the actual voltage of the power equipment ; The voltage change is linearly transformed B, and the formula is: ， in, It is the reference voltage value of the power grid under normal working condition; Calculate the second derivative of load data ;in is the load data at position x and time t; Calculate the spatial derivative of photovoltaic power data ;in is the photovoltaic power data at position x and time t; The second-order derivative of load data and the spatial derivative of photovoltaic power data are combined to construct the comprehensive impact factor C of spatial variation. The formula is: ， in, and are the minimum and maximum spatial positions of the distribution network, respectively, and x is the spatial position; Construct a photovoltaic carrying capacity evaluation model to evaluate the photovoltaic carrying capacity of the i-th power equipment in a typical mountain distribution network with multiple dimensions of source, grid and load. , the formula is: ， in, and are the start and end time of the evaluation period, respectively.

2. The source-grid-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity assessment method according to claim 1, characterized in that: The grid operation data includes grid operation stability, grid load and photovoltaic access data; The grid operation data is compared with the typical mountain range one by one. If the grid operation data meets the typical mountain range at the same time, it is determined to be a typical mountain with multiple dimensions of source, grid and load; if the grid operation data does not meet the typical mountain range, it is determined to be a common mountain with multiple dimensions of source, grid and load; The identifying of multi-dimensional typical mountains of source, grid and load includes setting a typical mountain range based on historical grid operation data and expert experience, and the typical mountain range includes grid operation stability, grid load and photovoltaic access range.

3. The source-grid-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity assessment method according to claim 2 is characterized by: The collecting of grid operation data of the multi-dimensional typical mountain distribution network includes installing smart meters in photovoltaic power stations within the source-grid-load multi-dimensional typical mountain distribution network area to collect photovoltaic power generation power, power load, power consumption data and voltage data of each power equipment in real time, and installing solar radiation meters and temperature sensors in photovoltaic power stations to collect environmental data in real time, including solar radiation intensity and temperature data; The spatial location of each power device is collected through GPS equipment.

4. The source-grid-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity assessment method according to claim 3 is characterized by: The preprocessing includes calculating load demand data based on the collected power consumption data. ; Linear interpolation was used to fill in missing data for photovoltaic power generation, power load, power consumption, voltage, environment, and load demand data; Use the z-score method to detect and delete outliers in the photovoltaic power generation, power load, power consumption, voltage, environment and load demand data; Use a moving average filter to denoise the photovoltaic power generation, power load, power consumption, voltage, environment and load demand data; The denoised photovoltaic power generation, power load, power consumption, voltage, environment and load demand data are standardized.

5. The source-grid-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity assessment method according to claim 4, characterized in that: The analysis and adjustment of the photovoltaic load capacity assessment results includes randomly collecting historical data within a period of time from the actual operating distribution network; Use the weighted average method to calculate the actual observed values ​​of historical data; The historical data includes photovoltaic power generation data, load demand data, environmental data and voltage data and is pre-processed to generate a test set; Inputting the test set into the photovoltaic carrying capacity evaluation model to obtain an evaluation result of the photovoltaic carrying capacity; The observation threshold is set based on the historical observation value, and the error value is obtained by subtracting the actual observation value from the evaluation result of the photovoltaic carrying capacity. The error value is compared with the observation threshold. If the error value is greater than the observation threshold, the temperature range in the environmental impact factor is adjusted; if the error value is less than or equal to the observation threshold, continuous observation is carried out.

6. The source-grid-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity assessment method according to claim 5, characterized in that: The construction of a visualization interface to display the results of the photovoltaic carrying capacity in real time includes using D3.js to construct a visualization interface, using a data visualization tool to display the photovoltaic carrying capacity evaluation results and error values ​​in real time, and using Plotly.js to draw a line graph of the photovoltaic carrying capacity evaluation results that is updated in real time for real-time display; The data generated during the storage of data asset assessment and access control include storing photovoltaic power generation power, power load, power consumption, voltage, environment, load demand data and photovoltaic carrying capacity assessment results in the database in chronological order, and setting security access measures. The database will back up the stored data in the cloud and regularly perform integrity checks on the stored data and backup data.

7. A source-grid-load multi-dimensional typical mountain distribution network photovoltaic carrying capacity assessment system using the method as described in any one of claims 1 to 6, characterized in that: The acquisition unit identifies multi-dimensional typical mountainous areas of source, grid and load, and collects the grid operation data of multi-dimensional typical mountainous distribution networks for pre-processing; The analysis unit builds a photovoltaic carrying capacity assessment model to assess the photovoltaic carrying capacity of a typical mountain distribution network in multiple dimensions of source, grid and load, and analyzes and adjusts the photovoltaic carrying capacity assessment results; The visualization unit builds a visualization interface to display the results of PV carrying capacity in real time, stores the data generated during the data asset assessment process and performs access control.

8. A computer device comprising: Memory and processor; The memory stores a computer program, wherein the processor implements the steps of any one of the methods of claims 1-6 when executing the computer program.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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